mirror of
https://github.com/clearlinux/graphene.git
synced 2026-09-06 05:41:37 +00:00
Michał Kowalczyk
6c7ff2d3a6
We decided to merge the sample app integrations submodule back because working with git submodules turned out to be really painful. The only blocker for this was the fact, that previously it contained a lot of binary blobs and copy-pasted sources, but this was cleaned up recently. Credits: (authors of particular integration examples, extracted from commits and PR history in https://github.com/oscarlab/graphene-tests) apache: Chia-Che Tsai <chiache@tamu.edu>, Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> bash: Chia-Che Tsai <chiache@tamu.edu>, Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> blender: borysp <borysp@invisiblethingslab.com> busybox: borysp <borysp@invisiblethingslab.com> capnproto: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> curl: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> gcc: Thomas Knauth <thomas.knauth@intel.com> lighttpd: Chia-Che Tsai <chiache@tamu.edu>, Thomas Knauth <thomas.knauth@intel.com> lmbench: Chia-Che Tsai <chiache@tamu.edu> memcached: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> nginx: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> nodejs: jack.wxz <jack.wxz@alibaba-inc.com> nodejs-express-server: Eduardo Rodriguez <erodrig@us.ibm.com> openvino: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> python-scipy-insecure: Chia-Che Tsai <chiache@tamu.edu>, Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> python-simple: Chia-Che Tsai <chiache@tamu.edu>, Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> pytorch: Thomas Knauth <thomas.knauth@intel.com> r: Chia-Che Tsai <chiache@tamu.edu> redis: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> tensorflow: Thomas Knauth <thomas.knauth@intel.com> LTP was moved to LibOS/shim/test/ltp. It was recently rewritten by Wojtek Porczyk <woju@invisiblethingslab.com>.
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
# This PyTorch image classification example is based off
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# https://www.learnopencv.com/pytorch-for-beginners-image-classification-using-pre-trained-models/
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from torchvision import models
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import torch
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# Load the model.
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alexnet = models.alexnet(pretrained=True)
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# Prepare a transform to get the input image into a format (e.g., x,y dimensions) the classifier
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# expects.
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from torchvision import transforms
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transform = transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225]
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)])
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# Load the image.
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from PIL import Image
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img = Image.open("input.jpg")
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# Apply the transform to the image.
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img_t = transform(img)
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# Magic (not sure what this does).
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batch_t = torch.unsqueeze(img_t, 0)
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# Prepare the model and run the classifier.
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alexnet.eval()
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out = alexnet(batch_t)
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# Load the classes from disk.
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with open('classes.txt') as f:
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classes = [line.strip() for line in f.readlines()]
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# Sort the predictions.
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_, indices = torch.sort(out, descending=True)
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# Convert into percentages.
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percentage = torch.nn.functional.softmax(out, dim=1)[0] * 100
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# Print the 5 most likely predictions.
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print([(classes[idx], percentage[idx].item()) for idx in indices[0][:5]])
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